How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

• URL: http://arxiv.org/abs/2204.10820v1
• Date: Fri, 22 Apr 2022 16:58:29 GMT
• Title: How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign
• Authors: Henrika Langen and Martin Huber
• Abstract summary: We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retail company. Our study provides a use case for the application of causal machine learning in business analytics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retail company. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across subgroups of customers, e.g. across clients with relatively high vs. low previous purchases. Finally, we use optimal policy learning to learn (in a data-driven way) which customer groups should be targeted by the coupon campaign in order to maximize the marketing intervention's effectiveness in terms of sales. Our study provides a use case for the application of causal machine learning in business analytics, in order to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.

Related papers

• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study [2.126171264016785]
  We develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online retailer in China, that is JD. Our results reveal that customers' product choice is insensitive to the promised delivery time, but this factor significantly impacts customers' order quantity. We identify product classes for which certain discounting approaches are more effective and provide recommendations on better use of different discounting tools.
  arXiv  Detail & Related papers  (2022-12-01T19:37:29Z)
• Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions [5.50110172922112]
  We propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget.
  arXiv  Detail & Related papers  (2022-07-23T07:13:57Z)
• Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
  A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times. This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
  arXiv  Detail & Related papers  (2022-02-26T02:07:25Z)
• Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity [54.26540039514418]
  This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
  arXiv  Detail & Related papers  (2021-11-11T05:36:39Z)
• Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC) [0.0]
  This study compared machine learning and lexicon-based approaches to the sentiment analysis. It was found that the approaches were dissimilar in terms of accuracy; the machine learning method yielded higher accuracy. It was also found that companies should be aware of their consumers' sentiments and choose the right person every time they think of a campaign.
  arXiv  Detail & Related papers  (2021-09-26T15:28:35Z)
• Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments [73.7716728492574]
  We study the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers' utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. We develop novel econometric instruments that capture product differentiation (isolation) based on deep-learning models of user-generated reviews.
  arXiv  Detail & Related papers  (2021-02-20T22:58:54Z)
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
  We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
  arXiv  Detail & Related papers  (2020-07-14T06:06:41Z)
• Do Interruptions Pay Off? Effects of Interruptive Ads on Consumers Willingness to Pay [79.9312329825761]
  We present the results of a study designed to measure the impact of interruptive advertising on consumers willingness to pay for products bearing the advertiser's brand. Our results contribute to the research on the economic impact of advertising, and introduce a method of measuring actual (as opposed to self-reported) willingness to pay in experimental marketing research.
  arXiv  Detail & Related papers  (2020-05-14T09:26:57Z)
• Interpretable Personalization via Policy Learning with Linear Decision Boundaries [14.817218449140338]
  effective personalization of goods and services has become a core business for companies to improve revenues and maintain a competitive edge. This paper studies the personalization problem through the lens of policy learning. We propose a class of policies with linear decision boundaries and propose learning algorithms using tools from causal inference.
  arXiv  Detail & Related papers  (2020-03-17T05:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.